Synthetic logs generator for fraud detection in mobile transfer services

Abstract

Mobile payments become more and more popular and thus are very attractive targets for fraudsters. As the latter always find new ways to commit crimes and avoid detection, research in the field of fraud is always evolving. However, transactional data and feedback from existing services are lacking. This article addresses this issue by proposing a synthetic data generator. Our idea is to model the behavior of various actors to generate testing data that researchers can use to evaluate approaches for identifying fraudulent transactions. This paper presents our approach and prototype. The logs generator was evaluated by comparing the generated synthetic logs with real ones.